About the job
Job Title: Team Lead, Data Analytics
Department: Finance
Reports to: VP, Finance
Job Purpose
To lead the design and delivery of advanced analytics, financial modelling, and data-driven insights that support strategic decision-making across revenue growth, customer behaviour, redemption patterns, and operational performance. The role combines business intelligence, statistical modelling, and strategic finance support, enabling leadership to make informed decisions in a high-growth fintech and loyalty platform environment.
Key Responsibilities
Revenue & Financial Modelling
- Develop dynamic revenue forecasting models incorporating factors such as points issuance growth, customer penetration rates, partner acquisition, and campaign performance.
- Perform sensitivity analysis on key business drivers including pricing changes, sales growth assumptions, and partner prepayment structures.
- Model revenue scenarios (Base, Slow Growth, Aggressive Expansion) to support strategic planning.
- Support 12–18 month financial and revenue forecasts for budgeting and planning processes.
Redemption & Liability Analytics
- Analyse historical redemption patterns and customer behaviour trends.
- Model redemption rate sensitivity scenarios (e.g., 20%, 50%, 75%, 90% redemption rates).
- Forecast future redemption liabilities and monitor exposure levels.
- Analyse the potential liquidity impact of redemption spikes or promotional campaigns.
Advanced Data Analytics & Predictive Modelling
- Develop cohort and segmentation models to analyse user behaviour and engagement.
- Perform regression analysis to identify key drivers of revenue and platform activity.
- Conduct Customer Lifetime Value (CLV) modelling.
- Develop predictive models for:
o Customer churn
o Redemption probability
o Campaign performance and ROI
- Analyse large transactional datasets to uncover business and operational insights.
Business Intelligence & Reporting
- Develop and maintain automated dashboards and reporting tools using platforms such as Power BI or Tableau.
- Ensure executives and operational teams have access to accurate and timely data insights.
- Present analytical insights and scenario modelling results to senior leadership and the executive team.
- Translate complex data analysis into clear commercial recommendations.
Strategic Finance & Investor Support
- Support investor reporting, board reporting, and strategic planning with analytical insights.
- Collaborate with Finance, Product, and Commercial teams to support data-driven decision making.
- Provide analytical input into new product launches, campaigns, and partner strategies.
Data Governance & Analytics Leadership
- Lead and mentor members of the data analytics function where applicable.
- Establish data quality standards and governance frameworks.
- Work with technology teams to improve data infrastructure and reporting efficiency.
- Promote a data-driven culture across the organization.
Qualifications
- Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, Economics, Computer Science, or a related quantitative field.
- 5+ years experience in data analytics, business intelligence, or financial modelling.
- Experience in fintech, loyalty platforms, e-commerce, or other high-transaction environments is preferred.
- Demonstrated experience in financial modelling, predictive analytics, and scenario analysis.
Required Skills
- Advanced SQL and database querying.
- Proficiency in Python or R for statistical analysis and predictive modelling.
- Experience with data visualization tools (Power BI, Tableau, or similar).
- Advanced Excel financial modelling.
- Strong understanding of statistics, probability, and predictive modelling techniques.
- Experience working with large transactional datasets and data pipelines.
- Strong communication skills with the ability to translate data into business insights.
KPIs
Analytics & Insights
- Accuracy of revenue and redemption forecasting models.
- Delivery of executive analytics dashboards and reports.
Business Impact
- Contribution of analytics insights to revenue growth or cost optimisation initiatives.
- Adoption of analytics insights in strategic decision-making.
Data Governance
- Improvement in data quality and reporting efficiency.
- Reduction in manual reporting processes through automation.
Strategic Support
- Timely delivery of investor and executive analytical reports.
